Transmission of text, images, voice and speech signals across communication channels, including the Internet, is increasing rapidly, as is the provision of multimedia services capable of accommodating various types of information, such as text, images and music. Multimedia signals, including speech and music signals, require a broad bandwidth at the time of transmission. Therefore, to transmit multimedia data, including text, images and audio, it is highly desirable that the data is compressed.
Compression of digital speech and audio signals is well known. Compression is generally required to efficiently transmit signals over a communications channel, or to store compressed signals on a digital media device, such as a solid-state memory device or computer hard disk.
A fundamental principle of data compression is the elimination of redundant data. Data can be compressed by eliminating redundant temporal information such as where a sound is repeated, predictable or perceptually redundant. This takes into account human insensitivity to high frequencies.
Generally, compression results in signal degradation, with higher compression rates resulting in greater degradation. A bit stream is called scalable when parts of the stream can be removed in a way that the resulting sub-stream forms another valid bit stream for some target decoder, and the sub-stream represents the source content with a reconstruction quality that is less than that of the complete original bit stream but is high when considering the lower quantity of remaining data. Bit streams that do not provide this property are referred to as single-layer bit streams. The usual modes of scalability are temporal, spatial, and quality scalability. Scalability allows the compressed signal to be adjusted for optimum performance over a band-limited channel.
Scalability can be implemented in such a way that multiple encoding layers, including a base layer and at least one enhancement layer, are provided, and respective layers are constructed to have different resolutions.
While many encoding schemes are generic, some encoding schemes incorporate models of the signal. In general, better signal compression is achieved when the model is representative of the signal being encoded. Thus, it is known to choose the encoding scheme based upon a classification of the signal type. For example, a voice signal may be modeled and encoded in a different way to a music signal. However, signal classification is generally a difficult problem.
An example of a compression (or “coding”) technique that has remained very popular for digital speech coding is known as Code Excited Linear Prediction (CELP), which is one of a family of “analysis-by-synthesis” coding algorithms. Analysis-by-synthesis generally refers to a coding process by which multiple parameters of a digital model are used to synthesize a set of candidate signals that are compared to an input signal and analyzed for distortion. A set of parameters that yield the lowest distortion is then either transmitted or stored, and eventually used to reconstruct an estimate of the original input signal. CELP is a particular analysis-by-synthesis method that uses one or more codebooks that each essentially comprises sets of code-vectors that are retrieved from the codebook in response to a codebook index.
In modern CELP coders, there is a problem with maintaining high quality speech and audio reproduction at reasonably low data rates. This is especially true for music or other generic audio signals that do not fit the CELP speech model very well. In this case, the model mismatch can cause severely degraded audio quality that can be unacceptable to an end user of the equipment that employs such methods.
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